Diagnostic apparatus for generating verification data including at least one piece of abnormal data based on normal data

ABSTRACT

A diagnostic apparatus of the invention acquires normal data related to an operating state during normal operation of an industrial machine, stores the normal data, generates a learning model by learning based on the stored normal data, and performs an estimation process for normality or abnormality of an operation of the industrial machine using the learning model. The diagnostic apparatus of the invention further generates verification data including at least one piece of abnormal data based on the stored normal data to verify validity of the learning model on receiving a result of the estimation process using the learning model based on the verification data.

RELATED APPLICATION

The present application claims priority to Japanese Patent ApplicationNumber 2019-182478 filed on Oct. 2, 2019, the disclosure of which ishereby incorporated by reference herein in its entirety.

BACKGROUND OF THE INVENTION 1. Field of the Invention

The present application relates to a diagnostic apparatus.

2. Description of the Related Art

In a manufacturing site such as a factory, in order to monitor anoperating state of an industrial machine such as a robot or a machinecool installed on a production line and prevent the production line fromstopping, and in order to be able to promptly restore the productionline when the production line is stopped, an apparatus for diagnosingthe operating state of the industrial machine has been introduced.

The apparatus for diagnosing the operating state of the industrialmachine, for example, monitors data such as a position, a speed andtorque of a motor detected by each industrial machine via a network anddata such as sound and an image detected by a sensor attached to theindustrial machine via the network, and diagnoses that an abnormalityhas occurred in an operation of the industrial machine when there is atendency indicating the abnormality in the monitored data (inparticular, JP 6453504 B1 and JP 2019-012473 A). In order for thediagnostic apparatus to make such a diagnosis, it is necessary togenerate in advance a predetermined model for determining whether theoperation of the industrial machine is normal or abnormal.

Examples of the model for determining normality/abnormality of theoperation of the industrial machine include: (1) a model for determininghow close to a data group detected while the industrial machine is inabnormal operation, (2) a model for determining how far apart from whichof boundaries of data groups acquired during normal operation/abnormaloperation, respectively, and (3) a model for determining how far apartfrom a data group detected while the industrial machine is in normaloperation. In the case of the models (1) and (2), it is necessary todetect and store data while the industrial machine is in abnormaloperation. However, abnormal operation of the industrial machine isrelated to failure in many cases, and it is difficult to collect datanecessary to generate a model for determination. On the contrary, in thecase of the model (3), because data can be detected and stored while theindustrial machine is in normal operation, the diagnostic apparatus canrelatively easily collect the data.

When the diagnostic apparatus performs an operation determination basedon a data group while the industrial machine is in normal operation, adata group collected to set a determination model needs to cover anentire range in which the industrial machine is in normal operation tosome extent. A simple example is illustrated in FIGS. 8A and 8B fordescription. For example, setting a model for determiningnormality/abnormality of the industrial machine using position data andtorque data of the motor acquired when the industrial machine is innormal operation is considered. Note that in FIGS. 8A and 8B, datadetected during normal operation by the diagnostic apparatus isindicated by a circle dot D1, an original range of data detected duringnormal operation is indicated within a frame of a solid line D2, and anexpected range of data detected during normal operation is indicatedwithin a frame of a dashed line D3.

Here, as illustrated in each of FIGS. 8A and 8B, when a set of positiondata and torque data detected from a motor of an industrial machine iswithin a solid line circle, it is presumed than the operation of theindustrial machine is normal. To mechanically generate such a modelbased on the data acquired during normal operation, a set of positiondata and torque data of the motor during normal operation of theindustrial machine is collected. As a result, as illustrated in FIG. 8A,when data can be acquired so as to cover the entire original range, itis possible to mechanically predict a model close to the original rangeD2 of the data D1 detected during normal operation. However, forexample, when only data deviated from the original range can be acquiredas illustrated in FIG. 8B, only a condition far from the original rangeof the data detected during normal operation (that is, range D3) can bemechanically predicted as indicated by a dotted circuit in FIG. 8B.

A comparatively simple example is illustrated in FIGS. 8A and 8B.However, in practice, in a case where the diagnostic apparatusdetermines normality/abnormality of the operation of the industrialmachine based on a change pattern of time series data such as position,torque or sound, a problem becomes more complicated. In addition, forexample, a similar problem occurs in the form of over-learning orconvergence to a local solution even in the case of using unsupervisedlearning, which is one of machine learning schemes.

To solve such a problem, it is possible to consider a method in whichvalidity of a generated model is verified, and when an invalid model isgenerated, data is reselected to regenerate another model. Here, whendata detected during normal and abnormal operation of the industrialmachine is collected, the collected data can be divided into modelgeneration data and verification data to verify a model generated by themodel generation data by means of the verification data. However, in acase where only data detected during normal operation of the industrialmachine is collected, even when the data is divided into the modelgeneration data and the verification data, validity of the divisionbecomes a problem with a certain probability. In addition, because thereis no data during abnormal operation, a response of the generated modelto abnormal data cannot be verified.

SUMMARY OF THE INVENTION

Therefore, a method for verifying validity of a model generated usingnormal data detected during normal operation in desired.

To verify validity of a model generated using normal data, a diagnosticapparatus according to the invention generates abnormal data obtained byadding an expected change to normal data, and then uses the generatedabnormal data so verify validity of the model, thereby solving the aboveproblem.

According to the invention, the diagnostic apparatus for diagnosing anoperating state of an industrial machine includes a data acquisitor foracquiring normal data related to an operating state during a normaloperation of the industrial machine; an acquired data storage forstoring the normal data acquired by the data acquisitor; a learner forgenerating a learning model by learning based on the normal data storedin the acquired data storage; an estimator for performing an estimationprocess for normality or abnormality of an operation of the industrialmachine using the learning model; a verification data generator forgenerating verification data including at least one piece of abnormaldata based on the normal data stored in the acquired data storage; and averificator for verifying validity of the learning model on receiving aresult of the estimation process performed by the estimator using thelearning model based on the verification data.

According to the invention, it is possible to verify validity of a modelgenerated using normal data detected during normal operation.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other objects and characteristics of the invention will beapparent from the following description of embodiments with reference tothe accompanying drawings.

FIG. 1 is a diagram schematically illustrating a hardware configurationexample of a diagnostic apparatus according to toe invention;

FIG. 2 is a schematic functional block diagram of a first embodiment ofa diagnostic apparatus according to the invention;

FIGS. 3A and 3B are diagrams illustrating an example of generatingabnormal data by adding an impulse;

FIGS. 4A and 4B are diagrams illustrating an example of generatingabnormal data by adding a fixed value component;

FIGS. 5A and 5B are diagrams illustrating an example of generatingabnormal data by adding an ax b component;

FIGS. 6A and 6B are diagrams illustrating an example of generatingabnormal data due to a data value defect;

FIGS. 7A and 7B are diagrams illustrating an example of generatingabnormal data due to a sampling defect; and

FIGS. 8A and 8B are diagrams for description of a problem of generatinga model for operation diagnosis of an industrial machine.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

An embodiment of the invention will be described below with reference tothe drawings.

FIG. 1 is a diagram schematically illustrating a hardware configurationexample illustrating a main part of a diagnostic apparatus according tothe invention. For example, a diagnostic apparatus 1 of the inventioncan be mounted as a controller for controlling an industrial machinebased on a control program. In addition, the diagnostic apparatus 1 ofthe invention can be mounted on a personal computer installed in acontroller for controlling an industrial machine based on a controlprogram, a personal computer connected via a wired/wireless network tothe controller, a cell computer, a fog computer 6 or a cloud server 7.In the present embodiment, an example in which the diagnostic apparatus1 is mounted on the personal computer connected via the network to thecontroller is shown.

A central processing unit (CPU) 11 included in the diagnostic apparatus1 according to the present embodiment is a processor is configured tocontrol the diagnostic apparatus 1 as a whole. The CPU 11 reads a systemprogram stored in a read only memory (ROM) 12 via a bus 22 to controlthe entire diagnostic apparatus 1 according to the system program. Arandom access memory (RAM) 13 temporarily stores temporary data oncalculation or display, various data input from the outside and thelike.

A nonvolatile memory 14 may be configured to include a memory backed upby a battery (not illustrated), a solid state drive (SSD) and the like.Due to the configuration, a storage state is retained even when thepower of the diagnostic apparatus 1 is turned OFF. The nonvolatilememory 14 stores data read from an external device 72 via an interface15, data input via an input 71, data acquired from the industrialmachine via a network 5 and other data. The various data stored in thenonvolatile memory 14 may be loaded in the RAM 13 during execution/use.In addition, various system programs such as known analysis programs arewritten in the ROM 12 in advance.

The interface 15 is a component provided for connecting the CPU 11 inthe diagnostic apparatus 1 to the external device 72 such as a USBdevice. From the external device 72 side, for example, data related tothe operation of each industrial machine can be read. In addition, aprogram, setting data and so on edited in the diagnostic apparatus 1 canbe stored in external storage device or similar means via the externaldevice 72.

In the diagnostic apparatus 1, an interface 20 is a component providedfor connecting the CPU in the apparatus 1 and the wired or wirelessnetwork 5 to each other. An industrial machine 3, a fog computer, acloud server and other devices on may be connected to the network 5,thereby data is exchanged between the diagnostic apparatus 1 and thevarious devices connected to the network 5.

Each piece of data read on the memory, data obtained as a result ofexecution of a program, data output from a machine learning device 100described later and similar data are output via the interface 17 anddisplayed on a display 70. In addition, the input 71 configured with akeyboard, a pointing device or the like delivers an instruction, dataand so on based on an operation of an operator via an interface 18 tothe CPU 11.

In the diagnostic apparatus 1, an interface 21 is a component providedfor connecting the CPU 11 and the machine learning device 100 to eachother. The machine learning device 100 includes a processor 101 forcontrolling the entire machine learning device 100, a ROM 102 forstoring in particular a system program, a RAM 103 for temporary storagein each process related to machine learning and a nonvolatile memory 104particularly used in storage of a learning model. The machine learningdevice 100 can observe each piece of information (for example, dataindicating an operating state of the industrial machine 3) that can beacquired by the diagnostic apparatus 1 via the interface 21. Inaddition, the diagnostic apparatus 1 can acquire a processing resultoutput from the machine learning device 100 via the interface 21. Thediagnostic apparatus 1 can also store and display the acquired resultand further transmit the acquired result to another device via thenetwork 5 or a similar channel.

FIG. 2 illustrates functions of a first embodiment of a diagnosticapparatus 1 according to the invention as a schematic functional block.Each function of the present embodiment of the diagnostic apparatus 1 isactualized by the CPU 11 included in the diagnostic apparatus 1 and theprocessor 101 included in the machine learning device 100 shown in FIG.1 executing a system program and controlling an operation of each unitin the diagnostic apparatus 1 and the machine learning device 100.

The present embodiment of the diagnostic apparatus 1 includes a dataacquisitor 110, a model generation instructor 120, a verification datagenerator 130 and a verificator 140. In addition, the machine learningdevice 100 included in the diagnostic apparatus 1 includes a learner 106and an estimator 108. Furthermore, in the RAM 13 and the nonvolatilememory 14 in the diagnostic apparatus 1, an acquired data storage 210 isprepared in advance as an area for storing data acquired by the dataacquisitor 110 from the industrial machine 3 and similar devices. In theRAM 103 and the nonvolatile memory 104 in the machine learning device100, a learning model storage 109 is prepared in advance as an area forstoring a learning model generated by the learner 106.

The data acquisitor 110 executes a system program read from the ROM 12by the CPU 11 included in the diagnostic apparatus 1 shown in FIG. 1 .Execution of such a program is actualized mainly by performingarithmetic processing using the RAM 13 and the nonvolatile memory 14 bythe CPU 11 and input control processing by the interfaces 15, 18 and 20.The data acquisitor 110 acquires data detected during normal operationof the industrial machine 3. The data acquisitor 110 acquires variousdata such as position data, speed data, acceleration data and torquedata of the motor of the industrial machine 3, and sound data and imagedata detected by a sensor (not shown) attached to the industrial machine3. The data acquired by the data acquisitor 110 may be time series data.The data acquisitor 110 may directly acquire data from the industrialmachine 3 via the network 5. The data acquisitor 110 may acquire dataacquired and stored by the external device 72, the fog computer 6, thecloud server 7 and any other devices.

The model generation instructor 120 executes the system program readfrom the ROM 12 by the CPU 11 included in the diagnostic apparatus 1shown in FIG. 1 . Execution of such a program is actuarized mainly byperforming arithmetic processing using the RAM 13 and the nonvolatilememory 14 by the CPU 11 and input/output control processing by theinterface 21. The model generation instructor 120 generates learningdata using at least partly data stored in the acquired data storage 210according to an instruction from the operator via the input 71. Themodel generation instructor 120 further instructs the machine learningdevice 100 to generate a learning model based on the generated learningdata. The model generation instructor 120 may instruct the machinelearning device 100 to generate one learning model. Alternatively, themodel generation instructor 120 may instruct the machine learning device100 to generate a plurality of learning models. In this instance, themodel generation instructor 120 may generate a plurality of differentdata sets by repeatedly extracting a predetermined number of pieces ofdata from the data stored in the acquired data storage 210, for example,using a random number. When the model generation instructor 120generates a plurality of different data sets using the random number andso on, the model generation instructor 120 may further instruct themachine learning device 100 to generate a plurality of learning modelsusing the generated plurality of different data sets.

The learner 106 included in the machine learning device 100 executes asystem program read from the ROM 102 by the processor 101 included inthe machine learning device 100 shown in FIG. 1 . Execution of such aprogram is actualized mainly by the processor 101 performing arithmeticprocessing using the RAM 103 and the nonvolatile memory 104. The learner106 generates a learning model by performing machine learning usinglearning data received from the model generation instructor 120, andthen stores the generated learning model in the learning model storage109. Machine learning performed by the learner 106 is a kind of knownunsupervised learning. The learning model generated by the learner 106is obtained by learning a tendency of normal data acquired during normaloperation of the industrial machine 3. Examples of the learning modelgenerated by the learner 106 include an auto-encoder (self-encoder).

The estimator 108 included in the machine learning device 100 executesthe system program read from the ROM 102 by the processor 101 includedin the machine learning device 100 shown in FIG. 1 . Execution of such aprogram is actualized mainly by the processor 101 performing arithmeticprocessing using the RAM 103 and the nonvolatile memory 104. Theestimator 108 executes an estimation process using verification data forthe learning model stored in the learning model storage 109, based on aninstruction from the verificator 140, to output an estimation result.The estimator 108 may perform an estimation process by the knownunsupervised learning to output a score, such as a normality level or anabnormality level, as the estimation result.

Alternatively, the estimator 108 may output a vector value indicating anormality level, an abnormality level or the like as the estimationresult.

The verification data generator 130 executes a system program read fromthe ROM 12 by the CPU 11 included in the diagnostic apparatus 1 shown inFIG. 1 . Execution of such a program is actualized mainly by the CPU 11performing arithmetic processing using the RAM 13 and the nonvolatilememory 14. The verification data generator 130 generates verificationdata including at least a predetermined number of pieces of abnormaldata, based on data stored in the acquired data storage 210. Asdescribed above, the acquired data storage 210 stores normal datadetected by the data acquisitor 110 during normal operation of theindustrial machine 3. The verification data generator 130 generatesabnormal data by applying a predetermined change to this normal data.The verification data generated by the verification data generator 130may be a simple set of abnormal data. Alternatively, the verificationdata may include normal data and abnormal data at a predetermined ratio.

The verification data generator 130 may generate abnormal data by addinga predetermined impulse to normal data. FIGS. 3A and 3B depict anexample of generating abnormal data (FIG. 3B) by adding an impulse totime series data that is normal data (FIG. 3A). As shown in FIGS. 3A and3B, the verification data generator 130 may generate abnormal data byadding one impulse to one piece of time series data or a plurality ofimpulses to the piece of time series data. A position to which theimpulse is applied may be given on the instruction of the operator ormay be randomly determined. Furthermore, with regard to the magnitudeand width of the impulse, the magnitude presumed to exceed a normaloperating range may be set based on experience of the operator using theindustrial machine 3. The impulse may be a negative value. Note thatwhen a generation target of the abnormal data is image data, a dot of apredetermined color and a predetermined size may be added to apredetermined position.

The verification data generator 130 may generate abnormal data by addinga predetermined fixed value (direct current value) component to normaldata. FIGS. 4A and 4B depict an example of generating abnormal data(FIG. 4B) by adding a fixed value component to time series data that isnormal data (FIG. 4A). With regard to the magnitude of the fixed valuecomponent added to the time series data by the verification datagenerator 130, the magnitude presumed to exceed a normal operating rangemay be set based on an experience of the operator using the industrialmachine 3. The fixed value component added by the verification datagenerator 130 to the time series data may be a negative value. Note thatwhen a generation target of the abnormal data is image data, a colorcomponent of the entire image data may be changed by a predeterminedamount.

The verification data generator 130 may generate abnormal data by addinga predetermined ax+b component to normal data. FIGS. 5A and 5B are anexample of generating abnormal data. (FIG. 5B) by adding the ax+bcomponent to time series data that is normal data (FIG. 5A). With regardto coefficients a and b of the ax+b component added to the time seriesdata by the verification data generator 130, the magnitude presumed toexceed a normal operating range may be set based on experience of theoperator using the industrial machine 3. Note that when a generationtarget of the abnormal data is image data, color components of theentire image data may be changed to gradation.

The verification data generator 130 may generate abnormal data by addinga predetermined frequency component to normal data. The verificationdata generator 130 may generate abnormal data by adding one frequencycomponent to one piece of time series data or a plurality of frequencycomponents to the piece of time series data. With regard to thefrequency value and magnitude of the frequency component, the magnitudepresumed to exceed a normal operating range may be set based onexperience of the operator using the industrial machine 3. Note thatwhen a generation target of the abnormal data is image data, conversionmay be performed by adding a predetermined two-dimensional frequencycomponent to the entire image data.

The verification data generator 130 may generate abnormal data by addinga predetermined data value defect to normal data. FIGS. 6A and 6B are anexample of generating abnormal data (FIG. 6B) by adding the data valuedefect to time series data that is normal data (FIG. 6A). As shown inFIGS. 6A and 6B, the verification data generator 130 may generateabnormal data by adding one data value defect or a plurality of datavalue defects to one piece of time series data. A position to which thedata value defect is applied may be randomly determined or be given onthe instruction of the operator. Furthermore, with regard to a width ofthe data value defect, the magnitude presumed to exceed a normaloperating range may be set based on experience of the operator using theindustrial machine 3. Note that when a generation target of the abnormaldata is image data, a black or white point of a predetermined size maybe added to a predetermined position.

The verification data generator 130 may generate abnormal data by addinga predetermined sampling defect to normal data. FIGS. 7A and 7B depictan example of generating abnormal data (FIG. 7B) by adding the samplingdefect to time series data that is normal data (FIG. 7A). As shown inFIGS. 7A and 7B, the verification data generator 130 may generateabnormal data by adding one sampling defect or a plurality of samplingdefects to one piece of time series data. A position to which thesampling defect is applied may be given on the instruction of theoperator or may be randomly determined. Furthermore, with regard to awidth of the sampling defect, the magnitude presumed to exceed a normaloperating range may be set based on experience of the operator using theindustrial machine 3.

The verification data generator 130 may include the abnormal datagenerated by the plurality of methods described above in one set ofverification data. Alternatively, the verification data generator 130may generate abnormal data by combining the plurality of methodsdescribed above.

The verificator 140 is actualized by the CPU 11 included in thediagnostic apparatus 1 shown in FIG. executing a system program readfrom the ROM 12 and performing arithmetic processing's mainly using theRAM 13 and the nonvolatile memory 14. The verificator 140 verifiesvalidity of the learning model stored in the learning model storage 109using the verification data generated by the verification data generator130. The verificator 140 subsequently outputs a verification result.

For example, when one learning model is stored in the learning modelstorage 109, the verificator 140 instructs the estimator 108 to performestimation based on the verification data using the learning model, andoutputs an estimation result to, for example, the display 70. Theoperator determines the validity of the learning model by looking at theestimation result output to the display 70. In this instance, apredetermined conditional expression may be set in advance, after thatthe verificator 140 may determine that the learning model is invalidwhen the conditional expression is dissatisfied. When the verificator140 determines that the learning model is invalid, the verificator 140may further instruct the model generation instructor 120 to regeneratethe learning model. In addition, the verificator 140 may calculate aknown machine learning evaluation value such as an ROC curve or an AUCvalue and display the evaluation value on the display 70. When theoperator confirms display of such a verification result and determinesthat a valid learning model is generated, the operator may use thelearning model stored in the learning model storage 109 for the actualstate determination of the industrial machine. On the contrary, when itis determined that the valid learning model may not be generated, theoperator may instruct the model generation instructor 120 to regeneratethe learning model.

For example, when a plurality of learning models is stored in thelearning model storage 109, the verificator 140 may instruct theestimator 108 to perform estimation based on the verification data usingeach learning model to select a learning model in which an averageresult is estimated among the estimation results obtained by theestimator 108 as a valid learning model. The average of the estimationresults means that the estimation results obtained by inputting theverification data to the learning model indicate a median value that isnot significantly shifted from estimation results of other learningmodels. For example, the verificator 140 expresses, as amultidimensional vector, a plurality of estimation results obtained byusing a plurality of pieces of verification data using a learning model.Next, the verificator 140 performs a publicly known outlier test on theestimation results of the plurality of multidimensional vectors obtainedfrom the respective learning models. In this way, the verificator 140can use a learning model other than a learning model estimating anoutlier as a valid learning model that estimates a relatively averageresult. In addition, for example, the verificator 140 may express aplurality of estimation results obtained by using a plurality of piecesof verification data using a learning model as a multidimensionalvector. The verificator 140 may use, as a valid learning model thatestimates a relatively average result, a learning model that estimates aresult in which a distance from an average vector of inference resultsof a plurality of multidimensional vectors obtained from each learningmodel is small. The verificator 140 may automatically select a learningmodel estimating the most average result as a valid learning model, oroutput some learning models indicating a relatively average estimationresult to the display 70 so that the operator can select a validlearning model from the output learning models.

Hereinafter, a schematic description will be given of an alternativeembodiment that can be adopted by the diagnostic apparatus of theinvention. In addition to setting values such as the magnitude of theimpulse or the fixed value component at the time of generating theabnormal data from the normal data, and the frequency value or themagnitude of the frequency component to values based on experience ofthe operator, for example, when a small amount of abnormal data isstored in the fog computer, the cloud server or the like, theverification data generator 130 included in the alternative embodimentof the diagnostic apparatus 1 may analyze the abnormal data to determineand use the magnitude of the impulse data and the fixed value data to bedetected as abnormal, the frequency value and the magnitude of thefrequency component, and the like. It is difficult to collect a largeamount of abnormal data. However, a small number of abnormal data can becollected on a network to which many industrial machines 3 areconnected. Therefore, by analyzing and using the tendency of impulses,fixed value components, and frequency components detected as abnormalfrom a small number of abnormal data, it is possible to eliminate theneed for setting based on the experience of the operator.

The present embodiment of the diagnostic apparatus 1 having theabove-mentioned constitution makes a predetermined change to normal dataacquired during normal operation of the industrial machine 3 to generateabnormal data, thereby generating data used in verification of alearning model. For this reason, it is unnecessary to collect apredetermined number of pieces of abnormal data, which are difficult tocollect, so that validity of the learning model can be easily verified.

Even though some embodiments of the invention has been described above,the invention is not limited to only the above-mentioned embodiments.The invention can be implemented in various modes by making appropriatechanges.

The invention claimed is:
 1. A diagnostic apparatus for diagnosing anoperating state of an industrial machine, the diagnostic apparatuscomprising: a data acquisitor for acquiring normal data related to anoperating state during a normal operation of the industrial machine; anacquired data storage for storing the normal data acquired by the dataacquisitor; a learner for generating a learning model by learning basedon the normal data stored in the acquired data storage; an estimator forperforming an estimation process for normality or abnormality of anoperation of the industrial machine using the learning model; averification data generator for generating verification data includingat least one piece of abnormal data based on the normal data stored inthe acquired data storage; and a verificator for verifying validity ofthe learning model on receiving a result of the estimation processperformed by the estimator using the learning model based on theverification data, wherein the learner generates a plurality of learningmodels, the verificator causes the estimator to perform estimation basedon the verification data using each of the plurality of learning models,and the verificator selects an average learning model from estimationresults obtained by the estimator as a valid learning model.
 2. Adiagnostic apparatus for diagnosing an operating state of an industrialmachine, the diagnostic apparatus comprising: a data acquisitor foracquiring normal data related to an operating state during a normaloperation of the industrial machine; an acquired data storage forstoring the normal data acquired by the data acquisitor; a learner forgenerating a learning model by learning based on the normal data storedin the acquired data storage; an estimator for performing an estimationprocess for normality or abnormality of an operation of the industrialmachine using the learning model; a verification data generator forgenerating verification data including at least one piece of abnormaldata based on the normal data stored in the acquired data storage; and averificator for verifying validity of the learning model on receiving aresult of the estimation process performed by the estimator using thelearning model based on the verification data, wherein the verificatorcauses the estimator to perform estimation based on the verificationdata using the learning model, and the verificator determines that thelearning model is invalid to regenerate a learning model when anestimation result obtained by the estimator dissatisfies a predeterminedcondition.
 3. A diagnostic apparatus for diagnosing an operating stateof an industrial machine, the diagnostic apparatus comprising: a dataacquisitor for acquiring normal data related to an operating stateduring a normal operation of the industrial machine; an acquired datastorage for storing the normal data acquired by the data acquisitor; alearner for generating a learning model by learning based on the normaldata stored in the acquired data storage; an estimator for performing anestimation process for normality or abnormality of an operation of theindustrial machine using the learning model; a verification datagenerator for generating verification data including at least one pieceof abnormal data by applying a predetermined change to the normal datastored in the acquired data storage; and a verificator for verifyingvalidity of the learning model on receiving a result of the estimationprocess performed by the estimator using the learning model based on theverification data, wherein the verification data generator is configuredto generate abnormal data by adding to normal data at least one of animpulse, a fixed value component, an ax+b component, a frequencycomponent, a data value defect or a sampling defect, the diagnosticapparatus further comprises a display, wherein when a generation targetof the abnormal data is an image, the verification data generator isconfigured to add a dot of a predetermined color and a predeterminedsize to a predetermined position on the display, when the impulse isadded to normal data, change a color component of entire image data by apredetermined amount when the fixed value component is added to normaldata, change color components of the entire image data to gradation whenthe ax+b component is added to normal data, add a predeterminedtwo-dimensional frequency component to the entire image data when thefrequency component is added to normal data, and add a black or whitepoint of a predetermined size to a predetermined position on the displaywhen the data value defect is added to normal data, and the verificatoris configured to output an estimation result obtained by the estimatorto the display.
 4. The diagnostic apparatus according to claim 3,wherein the learning model is an auto-encoder.
 5. The diagnosticapparatus according to claim 3, wherein the verification data is a setof abnormal data, or includes normal data and abnormal data at apredetermined ratio.